Time series patterns are patterns of data points made over continuous time intervals out of successive measurements across said intervals, using equal spacing between every two consecutive measurements and with each time unit within the time intervals having at most one data point. Examples of time series patterns are audio patterns, such as sound patterns and human speech patterns. It may be useful to detect specific time series patterns, for example in order to recognize particular events or contexts (e.g., starting a car or being present in a running car) and to distinguish and identify different speakers. Furthermore, it may be useful to make such detections easier and more reliable.